Effect of interventions to reduce wait times for diagnosis and treatment of sleep-disordered breathing in adults: A systematic review
Bibliographic record
Abstract
RATIONALE: Sleep disordered breathing (SDB) is associated with adverse health consequences that can be mitigated through timely and effective management. Guidelines provide wait time targets, but the evidence supporting them is unclear. The purpose of this systematic review was to explore the relationship between interventions to improve wait times for SDB and the effects on patient or provider outcomes.METHODS: A targeted search of medical databases was performed from database inception to June 2017. Included studies described an intervention intended to reduce wait times for the diagnosis or treatment of SDB and reported on a patient- or provider-level outcome.RESULTS: The search produced 2,944 abstracts and 51 articles underwent full text review. Ten articles were included in the final review. Five trials reported wait times to diagnosis and treatment, 3 studies described wait times for diagnosis only and 2 studies discussed time to treatment exclusively. All studies were of moderate methodological quality. Wait times were improved in most studies, but due to short follow-up periods a clear relationship with improved health outcomes was rarely established. The variety of interventions limited the characterization of specific wait-times reduction strategies that could reliably improve outcomes.CONCLUSIONS: This review highlights the scarcity of studies but suggests a possible clinical benefit of interventions to reduce wait times. However, generalizability is limited and the short follow-up periods likely underestimate potential positive impacts of reduced wait times. Further studies are needed to better characterize this relationship and to identify additional interventions to deliver more timely patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".